A new subjective procedure for evaluation and development of texture similarity metrics

Zujovic, Jana; Pappas, Thrasyvoulos N.; Neuhoff, David L.; Egmond, René van; Ridder, Huib de

doi:10.1109/ivmspw.2011.5970366

Cited by 7 publications

(16 citation statements)

References 8 publications

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“…Similarly Gabor and Wavelet Transform for Texture [12] and Moment Invariant Method and Fourier Descriptors [13] for shape feature are considered.…”

Section: Methodsmentioning

confidence: 99%

UNIQUE CBIR – A Unified Method of Retrieving Similar Images

UmaMaheswar¹,

Reddy²

2015

IJCA

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Content Based Image Retrieval (CBIR) is a method of extracting image features and calculate visual similarity based on these features. Lot of research activity is continuing for optimizing and improving the feature extraction methods focusing on colour, texture and shape features to minimize the semantic gap. Relevant Feedback (RF) is one variant of CBIR wherein the user provides feedback by selecting the most relevant image from retrieved images and make it a query image for the subsequent iterations. The RF methods improved the performance of the overall system in multiple iterations but the time taken for the total process increases on the other hand. This paper attempts to address the semantic gap problem by having the user interface in the forward path by adding some more key requirements of the user for narrowing down the search options thereby increasing the system performance and reducing the retrieval time.

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“…Similarly Gabor and Wavelet Transform for Texture [12] and Moment Invariant Method and Fourier Descriptors [13] for shape feature are considered.…”

Section: Methodsmentioning

confidence: 99%

UNIQUE CBIR – A Unified Method of Retrieving Similar Images

UmaMaheswar¹,

Reddy²

2015

IJCA

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“…For color images, Zujovic et al combine the grayscale metric value with a metric that compares the color compositions of two image regions. 61,65,66 They thus separate the color composition of a texture from the spatial structure, which to a large extend is reflected in the grayscale component. Note that this does not account for any pure chrominance structure, which is not typical for most natural textures.…”

Section: (A)mentioning

confidence: 99%

“…Note that this does not account for any pure chrominance structure, which is not typical for most natural textures. 65…”

Section: (A)mentioning

confidence: 99%

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The rough side of texture: texture analysis through the lens of HVEI

Pappas¹

2013

SPIE Proceedings

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We take a look at texture analysis research over the past 25 years, from the persective of the Human Vision and Electronic Imaging conference. We consider advances in the understanding of human perception of textures and the development of texture analysis algorithms for practical applications. We cover perceptual models and algorithms for image halftoning, texture discrimination, texture segmentation, texture analysis/synthesis, perceptually and structurally lossless compression, content-based retrieval, and sense substitution.

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“…Conditions of observation must be identical for all observers and the screen must be sufficiently large. 25,26 This work utilized concepts developed to assess results from fusion processes objectively.…”

Section: Evaluation Of Color Fusionmentioning

confidence: 99%

Bio-empirical mode decomposition: visible and infrared fusion using biologically inspired empirical mode decomposition

Sissinto

Ladeji-Osias

2013

Opt. Eng

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Abstract. Bio-EMD, a biologically inspired fusion of visible and infrared (IR) images based on empirical mode decomposition (EMD) and color opponent processing, is introduced. First, registered visible and IR captures of the same scene are decomposed into intrinsic mode functions (IMFs) through EMD. The fused image is then generated by an intuitive opponent processing the source IMFs. The resulting image is evaluated based on the amount of information transferred from the two input images, the clarity of details, the vividness of depictions, and range of meaningful differences in lightness and chromaticity. We show that this opponent processing-based technique outperformed other algorithms based on pixel intensity and multiscale techniques. Additionally, Bio-EMD transferred twice the information to the fused image compared to other methods, providing a higher level of sharpness, more natural-looking colors, and similar contrast levels. These results were obtained prior to optimization of color opponent processing filters. The Bio-EMD algorithm has potential applicability in multisensor fusion covering visible bands, forensics, medical imaging, remote sensing, natural resources management, etc.

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A new subjective procedure for evaluation and development of texture similarity metrics

Cited by 7 publications

References 8 publications

UNIQUE CBIR – A Unified Method of Retrieving Similar Images

UNIQUE CBIR – A Unified Method of Retrieving Similar Images

The rough side of texture: texture analysis through the lens of HVEI

Bio-empirical mode decomposition: visible and infrared fusion using biologically inspired empirical mode decomposition

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